In this paper, we consider the problem of irregular shapes tracking for multiple extended targets by introducing the Gaussian surface matrix (GSM) into the framework of the random finite set (RFS) theory. The Gaussian surface function is constructed first by the measurements, and it is used to define the GSM via a mapping function. We then integrate the GSM with the probability hypothesis density (PHD) filter, the Bayesian recursion formulas of GSM-PHD are derived and the Gaussian mixture implementation is employed to obtain the closed-form solutions. Moreover, the estimated shapes are designed to guide the measurement set sub-partition, which can cope with the problem of the spatially close target tracking. Simulation results show that the proposed algorithm can effectively estimate irregular target shapes and exhibit good robustness in cross extended target tracking.

Weak global navigation satellite system (GNSS) signal acquisition has been a limitation for high sensitivity GPS receivers. This paper modifies the traditional acquisition algorithms and proposes a new weak GNSS signal acquisition method using re-scaling and adaptive stochastic resonance (SR). The adoption of classical SR is limited to low-frequency and periodic signals. Given that GNSS signal frequency is high and that the periodic feature of the GNSS signal is affected by the Doppler frequency shift, classical SR methods cannot be directly used to acquire GNSS signals. Therefore, the re-scaling technique is used in our study to expand its usage to high-frequency signals and adaptive control technique is used to gradually determine the Doppler shift effect in GNSS signal buried in strong noises. The effectiveness of our proposed method was verified by the simulations on GPS L1 signals. The simulation results indicate that the new algorithm based on SR can reach −181 dBW sensitivity with a very short data length of 1 ms.

By applying phase-only technique in array antenna pattern synthesis, antenna arrays can form desired patterns with the use of phase shifters only. A novel phase-only pattern synthesis algorithm is proposed for the passive phased array seeker. This algorithm synthesizes the main beam of the antenna pattern hrough least-squares approximation, thus minimizing the errors between the actual and the desired main beams. The synthesis problem can be solved by applying gradient-descent optimization. The item for suppressing sidelobes is added to the above synthesis problem. To obtain a side lobe level as low as possible, the algorithm assigns different weights to different directions in the side lobe region. The algorithm is run repeatedly and the weights are adjusted adaptively according to the normalized power in the side lobe directions. Detailed examples are presented to demonstrate the accuracy and effectiveness of the proposed approach.

This paper provides a direct and fast acquisition algorithm of civilian long length (CL) codes in the L2 civil (L2C) signal. The proposed algorithm simultaneously reduces the number of fast Fourier transformation (FFT) correlation through hyper code technique and the amount of points in every FFT correlation by using an averaging correlation method. To validate the proposed acquisition performance, the paper applies this algorithm to the real L2C signal collected by the global positioning system (GPS) L2C intermediate frequency (IF) signal sampler—SIS100L2C. The acquisition results show that the roposed modified algorithm can acquire the code phase accurately with less calculation and its acquisition performance is better than the single hyper code method.

 It is understood that the sparse signal recovery with a standard compressive sensing (CS) strategy requires the measurement matrix known as a priori. The measurement matrix is, however, often perturbed in a practical application. In order to handle such a case, an optimization problem by exploiting the sparsity characteristics of both the perturbations and signals is formulated. An algorithm named as the sparse perturbation signal recovery algorithm (SPSRA) is then proposed to solve the formulated optimization problem. The analytical results show that our SPSRA can simultaneously recover the signal and perturbation vectors by an alternative iteration way, while the convergence of the SPSRA is also analytically given and guaranteed. Moreover, the support patterns of the sparse signal and structured perturbation shown are the same and can be exploited to improve the estimation accuracy and reduce the computation complexity of the algorithm. The numerical simulation results verify the effectiveness of analytical ones.

The energy efficiency (EE) for the full-duplex massive multi-input multi-output (MIMO) system is investigated. Given the transmit powers of both the uplink and the downlink, the closed-form solutions of the optimal number of antennas and the maximum EE are achieved in the high regime of the signal-to-noise ratio (SNR). It is shown that the optimal number of antennas and the maximum EE gets larger with the increase in user numbers. To further improve the EE, an optimization algorithm with low complexity is proposed to jointly determine the number of antennas and the transmit powers of both the uplink and the downlink. It is shown that, the proposed algorithm can achieve the system performance very close to the exhaustive search.

There are various heterogeneous networks for terminals to deliver a better quality of service. Signal system recognition and classification contribute a lot to the process. However, in low signal to noise ratio (SNR) circumstances or under time-varying multipath channels, the majority of the existing algorithms for signal recognition are already facing limitations. In this series, we present a robust signal recognition method based upon the original and latest updated version of the extreme learning machine (ELM) to help users to switch between networks. The ELM utilizes signal characteristics to distinguish systems. The uperiority of this algorithm lies in the random choices of hidden nodes and in the fact that it determines the output weights analytically, which result in lower complexity. Theoretically, the algorithm tends to offer a good generalization performance at an extremely fast speed of learning. Moreover, we implement the GSM/WCDMA/LTE models in the Matlab environment by using the Simulink tools. The simulations reveal that the signals can be recognized successfully to chieve a 95% accuracy in a low SNR (0 dB) environment in the time-varying multipath Rayleigh fading channel.

This paper presents an enhanced multi-baseline phase unwrapping algorithm by combining an unscented Kalman filter with an enhanced joint phase gradient estimator based on the amended matrix pencil model, and an optimal path-following strategy based on phase quality estimate function. The enhanced joint phase gradient estimator can accurately and effectively extract the phase gradient information of wrapped pixels from noisy interferograms, which greatly increases the performances of the proposed method. The optimal path-following strategy ensures that the proposed algorithm simultaneously performs noise suppression and phase unwrapping along the pixels with high-reliance to the pixels with low-reliance. Accordingly, the proposed algorithm can be predicted to obtain better results, with respect to some other algorithms, as will be demonstrated by the results obtained from synthetic data.

For a synthetic aperture radar (SAR) system mounted on a geostationary Earth orbit (GEO) satellite, the track can be curvilinear. Thus, a bistatic SAR system based up on geostationary transmitter and “receive-only” SAR system onboard airplanes, namely GEO spaceborne-airborne bistatic (GEO SA-Bi SAR), is significantly different from the traditional bistatic SAR. This paper mainly studies the resolution characteristic of the sliding spotlight GEO SA-Bi SAR system. irstly, the common azimuth coverage and coherent accumulated time are theoretically analyzed in detail. Then, based on the gradient method, the accurate two dimensional resolution of a GEO SA-Bi SAR system is analytically calculated. Finally, the simulation data show the correctness and effectiveness of the proposed resolution analysis method.

 For the issue of deterioration in detection performance caused by dynamically changing environment in ultra-wideband (UWB) multiple input multiple output (MIMO) radar, this paper proposes a novel adaptive waveform design which is aimed to improve the ability of discriminating target and clutter from the radar scene. Firstly, a sequence of Morlet wavelet pulses with frequency hopping and pulse position modulation by Welch-Costas array is designed. Then a waveform optimization solution is proposed which is achieved by applying the minimization mutual-information (MI) strategy. After that, with subsequent iterations of the algorithm, simulation results demonstrate that the optimal waveform design method brings an improvement in the target detection ability in the presence of noise and clutter.

A multiple-input multiple-output (MIMO) sonar can synthesize a large-aperture virtual uniform linear array (ULA) from a small number of physical elements. However, the large aperture is obtained at the cost of a great number of matched filters with much heavy computation load. To reduce the computation load, a MIMO sonar imaging method using a virtual sparse linear array (SLA) is proposed, which contains the offline and online processing. In the offline processing, the virtual ULA of the MIMO sonar is thinned to a virtual SLA by the simulated annealing algorithm, and matched filters corresponding to inactive virtual elements are removed. In the online processing, outputs of matched filters corresponding to active elements are collected for further multibeam processing and hence, the number of matched filters in the echo processing procedure is effectively reduced. Numerical simulations show that the proposed method can reduce the computation load effectively while obtaining a similar imaging performance as the traditional method.

Satellite constellation design for space optical systems is essentially a multiple-objective optimization problem. In this work, to tackle this challenge, we first categorize the performance metrics of the space optical system by taking into account the system tasks (i.e., target detection and tracking). We then propose a new non-dominated sorting genetic algorithm (NSGA) to maximize the system surveillance performance. Pareto optimal sets are employed to deal with the conflicts due to the presence of multiple cost functions. Simulation results verify the validity and the improved performance of the proposed technique over benchmark methods.

 The problem of cloud cooperation of military service providers (MSPs) is addressed for allocating limited resources to military service users (MSUs) that are geographically distributed. The MSPs, also called military organization clouds, are virtualized and encapsulated by the services they can offer and each of them contains different kinds of resources that MSU needs. The MSPs are also geographically dispersed. They are required to allocate their resources to the MSU complying with the corresponding quality of service (QoS), so that each MSU gathers the services it needs to guarantee its task to be implemented. The outline of military organization cloud cooperation is discussed and the method of service optimal selection is proposed based on QoS evaluation. The QoS evaluation method based on exponential approximation is put forward to include the users’ will. Simulation results verify the effectiveness of the proposed algorithm.

The backtracking search optimization algorithm (BSA) is one of the most recently proposed population-based evolutionary algorithms for global optimization. Due to its memory ability and simple structure, BSA has powerful capability to find global optimal solutions. However, the algorithm is still insufficient in balancing the exploration and the exploitation. Therefore, an improved adaptive backtracking search optimization algorithm combined with modified Hooke-Jeeves pattern search is proposed for numerical global optimization. It has two main parts: the BSA is used for the exploration phase and the modified pattern search method completes the exploitation phase. In particular, a simple but effective strategy of adapting one of BSA’s important control parameters is introduced. The proposed algorithm is compared with standard BSA, three state-of-the-art evolutionary algorithms and three superior algorithms in IEEE Congress on Evolutionary Computation 2014 (IEEE CEC2014) over six widely-used benchmarks and 22 real-parameter single objective numerical optimization benchmarks in IEEE CEC2014. The results of experiment and statistical analysis demonstrate the effectiveness and efficiency of the proposed algorithm.

 This paper studies the problem of the space station short-term mission planning, which aims to allocate the exe cuting time of missions effectively, schedule the corresponding resources reasonably and arrange the time of the as tronauts properly. A domain model is developed by using the ontology theory to describe the concepts, constraints and relations of the planning domain formally, abstractly and normatively. A method based on time iteration is adopted to solve the short-term planning problem. Meanwhile, the resolving strategies are proposed to resolve different kinds of conflicts induced by the constraints of power, heat, resource, astronaut and relationship. The proposed approach is evaluated in a test case with fifteen missions, thirteen resources and three astronauts. The results show that the developed domain ontology model is reasonable, and the time iteration method using the proposed resolving strategies can successfully obtain the plan satisfying all considered constraints.

How to deal with the collaboration between task decomposition and task scheduling is the key problem of the integrated manufacturing system for complex products. With the development of manufacturing technology, we can probe a new way to solve this problem. Firstly, a new method for task granularity quantitative analysis is put forward, which can precisely evaluate the task granularity of complex product cooperation workflow in the integrated manufacturing system, on the above basis; this method is used to guide the coarse-grained task decomposition and recombine the subtasks with low cohesion coefficient. Then, a multi-objective optimieation model and an algorithm are set up for the scheduling optimization of task scheduling. Finally, the application feasibility of the model and algorithm is ultimately validated through an application case study.

To be close to the practical flight process and increase the precision of optimal trajectory, a six-degree-of-freedom (6-DOF) trajectory is optimized for the reusable launch vehicle (RLV) using the Gauss pseudospectral method (GPM). Different from the traditional trajectory optimization problem which generally considers the RLV as a point mass, the coupling between translational dynamics and rotational dynamics is taken into account. An optimization problem is formulated to minimize a performance index subject to 6-DOF equations of motion, including translational and rotational dynamics. A two-step optimal strategy is then introduced to reduce the large calculations caused by multiple variables and convergence confinement in 6-DOF trajectory optimization. The simulation results demonstrate that the 6-DOF trajectory optimal strategy for RLV is feasible.

The attitude control system design and its control effect are affected considerably by the mass-property parameters of the spacecraft. In the mission of on-orbit servicing, as fuel is expended, or the payloads are added or removed, the center of mass will be changed in certain axe; consequently, some thrusters' directions are deviated from the center of mass (CM) in certain plane. The CM of assembled spacecraft estimation and thruster direction control are studied. Firstly, the attitude dynamics of the assembled spacecraft is established based on the Newton-Euler method. Secondly, the estimation can be identified by the least recursive squares algorithm. Then, a scheme to control the thrusters’ directions is proposed. By using the gimbal installed at the end of the boom, the angle of the thruster is controlled by driving the gimbal; therefore, thrusters can be directed to the CM again. Finally, numerical simulations are used to verify this scheme. Results of the numerical simulations clearly show that this control scheme is rational and feasible.

Currently, 1 bit or 2 bit signal quantization is widely used in satellite navigation software receivers. The bit-wise parallel algorithm has been proposed for 1 bit and 2 bit signal quantization, which performs correlation with high efficiency. In order to improve the performance of the correlator, this paper proposes a new 1.5 bit quantization method. Theoretical analyses are made from the aspects of complexity and quantization loss, and performance comparison between 1.5 bit quantization correlator and traditional correlators is discussed. The results show that the 1.5 bit quantization algorithm can save about 30 percent complexity under similar quantization loss, reduce more than 0.5 dB signal noise ratio (SNR) loss under similar complexity. It shows great performance improvement for correlators of satellite navigation software receivers.

In order to lower the power consumption and improve the coefficient of resource utilization of current cloud computing systems, this paper proposes two resource pre-allocation algorithms based on the “shut down the redundant, turn on the demanded” strategy here. Firstly, a green cloud computing model is presented, abstracting the task scheduling problem to the virtual machine deployment issue with the virtualization technology. Secondly, the future workloads of system need to be predicted: a cubic exponential smoothing algorithm based on the conservative control (CESCC) strategy is proposed, combining with the current state and resource distribution of system, in order to calculate the demand of resources for the next period of task requests. Then, a multi-objective constrained optimization model of power consumption and a low-energy resource allocation algorithm based on probabilistic matching (RA-PM) are proposed. In order to reduce the power consumption further, the resource allocation algorithm based on the improved simulated annealing (RA-ISA) is designed with the improved simulated annealing algorithm. Experimental results show that the prediction and conservative control strategy make resource pre-allocation catch up with demands, and improve the efficiency of real-time response and the stability of the system. Both RA-PM and RA-ISA can activate fewer hosts, achieve better load balance among the set of high applicable hosts, maximize the utilization of resources, and greatly reduce the power consumption of cloud computing systems.

How to efficiently measure the distance between two basic probability assignments (BPAs) is an open issue. In this paper, a new method to measure the distance between two BPAs is proposed, based on two existing measures of evidence distance. The new proposed method is comprehensive and generalized. Numerical examples are used to illustrate the effectiveness of the proposed method.

The traditional oriented FAST and rotated BRIEF (ORB) algorithm has problems of instability and repetition of keypoints and it does not possess scale invariance. In order to deal with these drawbacks, a modified ORB (MORB) algorithm is proposed. In order to improve the precision of matching and tracking, this paper puts forward an MOK algorithm that fuses MORB and Kanade-Lucas-Tomasi (KLT). By using Kalman, the object’s state in the next frame is predicted in order to reduce the size of search window and improve the real-time performance of object tracking. The experimental results show that the MOK algorithm can accurately track objects with deformation or with background clutters, exhibiting higher robustness and accuracy on diverse datasets. Also, the MOK algorithm has a good real-time performance with the average frame rate reaching 90.8 fps.

A general version of the inverted exponential distribution is introduced, studied and analyzed. This generalization depends on the method of Marshall-Olkin to extend a family of distributions. Some statistical and reliability properties of this family are studied. In addition, numerical estimation of the maximum likelihood estimate (MLE) parameters are discussed in details. As an application, some real data sets are analyzed and it is observed that the presented family provides a better fit than some other known distributions.

In some situations, the accelerated life test on environmental stress for electronic products is not easily implemented due to various restrictions, and thus engineers are lacking of data of the product life test. Concerning this problem, environmental life of the printed circuit board (PCB) board is calculated by way of physics of failure. Influences of thermal cycle and vibration on PCB and its components are studied. Based on the analysis of force and stress between components and the PCB board in thermal cycle events and vibration events, four life computing models of pins and soldered dots are established. The miller damage ratio is used to calculate the accumulated damage of a pin or a soldered dot, and then the environment life of the PCB board can be determined by the first failed one. Finally, an example is used to illustrate the models and their calculations.